本文目前仅提供英文版本。
AI in M&A Due Diligence: What Buyers Need to Assess and What Sellers Need to Disclose
AI is now a material factor in M&A due diligence. Buyers need to assess AI governance maturity, regulatory compliance exposure, IP ownership of AI-generated assets, data licensing risk, and vendor dependency. Sellers need to disclose AI systems, training data provenance, and known governance gaps. The due diligence checklist for AI-era transactions.
Key Takeaways
This article provides practical governance guidance verified against primary regulatory sources.
All facts and regulatory references have been verified as of May 2026.
"仅供参考。本文不构成法律、监管、财务或专业建议。如需具体指导，请咨询合格专家。"
AI due diligence in mergers and acquisitions is the structured assessment of AI-related risks, assets, liabilities, and governance maturity that buyers should conduct — and sellers should prepare for — in any transaction involving a company that uses or develops AI. In 2026, AI is a material factor in deal valuation, risk allocation, and post-merger integration. Aon's 2026 AI risk report identifies AI governance maturity as an emerging consideration in D&O underwriting. The EU AI Act creates compliance obligations that transfer with the business. Training data provenance, IP ownership of AI-generated assets, vendor dependency, and regulatory exposure are all deal-relevant issues that traditional due diligence frameworks do not adequately cover.
What buyers need to assess
AI due diligence should cover five domains. AI inventory and governance maturity: does the target have a complete inventory of AI systems, clear ownership and accountability, documented policies, and a governance framework that matches the risk level? Regulatory compliance exposure: is the target subject to the EU AI Act, GDPR AI obligations, sector-specific AI regulation (APRA, FCA, FDA), or US state AI laws — and is it compliant? Identify any pending or potential regulatory actions. Intellectual property: who owns the AI models, the training data, and the outputs? Are there licensing risks from training data sourced without proper rights? Has the target relied on open-source AI models with licence conditions that may affect commercial use? Vendor dependency: is the target critically dependent on a single AI vendor or cloud provider? What are the contractual terms, exit provisions, and concentration risks? Data assets and liabilities: what data does the target hold, how was it collected, what consent basis exists, and are there cross-border transfer mechanisms in place?
What sellers need to disclose
Sellers should prepare an AI disclosure package covering: a complete AI system inventory with risk classifications, all AI-related regulatory correspondence or investigations, known governance gaps or remediation plans, training data provenance documentation, AI vendor contracts and dependency analysis, any AI-related incidents, complaints, or litigation, employee AI usage policies and shadow AI assessment, and the status of any ongoing AI compliance programmes. Sellers who proactively prepare this documentation demonstrate governance maturity and reduce deal friction — buyers who discover undisclosed AI risks during due diligence will adjust valuation downward or walk away.
Post-merger integration considerations
AI governance integration post-merger requires harmonising AI policies and risk frameworks across both organisations, consolidating AI inventories, resolving vendor overlaps and concentration risks, aligning with the buyer's regulatory obligations (which may differ from the target's), and addressing any governance gaps identified during due diligence. The integration timeline should account for the regulatory calendar — EU AI Act compliance deadlines, sector-specific requirements, and jurisdictional variations that may affect the combined entity differently than either organisation alone.
Further reading: Aon — AI Risk 2026 | ISO 42001